AmuraAMURA Marketing
Glossary

LLMO Large Language Model Optimization

Working on the corpus a language model has already trained on — mentions, reviews, listings, partner sites, press — so the model has the right associations baked in by the time a user asks about your category.

In depth

LLMO is the upstream half of the AI presence stack. Models don't just retrieve at query time; they carry priors from training data. LLMO shapes those priors. Practically: identifying which sources a model already weights heavily in your category, then methodically becoming part of those sources — through digital PR, listings, partnerships, reviews, expert citations and signed thought leadership.

Why it matters

Retrieval-augmented generation can fix a few things at query time, but the model's baseline associations are set during training. LLMO is how you change those baselines.

Example

A B2B vendor identifies G2, Gartner peer reviews and three industry publications as the sources Claude and GPT-4 weight in their category. Six months of focused PR turns those sources into reliable citations of the vendor — and the vendor starts appearing in 'top 5 vendors for X' AI replies.

AI Visibility Audit

Do you know what AI
says about you?

Request an audit and discover how your brand appears when customers, partners and investors ask AI for solutions, recommendations, comparisons or vendors in your category.

Includes
  • 01Analysis across ChatGPT, Gemini, Perplexity, Copilot and Google AI Mode
  • 02Real comparison with your main competitors
  • 03Citations, mentions and source review
  • 04Detection of errors and incomplete information
  • 05Content and authority opportunities
  • 06Executive 30 / 60 / 90 day roadmap